Data Scientist
Summary
Builds ML models and analyzes time-series plant data using Seeq and Python to optimize manufacturing processes, predict maintenance, and improve quality.
Roles & Responsibilities
- Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities.
- Utilize the Seeq platform for: Time-series analysis; Root-cause investigations; Process monitoring and visualization
- Develop and deploy machine learning models for: Predictive maintenance; Process efficiency improvement; Quality and yield optimization
- Collaborate closely with plant, engineering, and operations teams to understand real-world process challenges.
- Translate business and operational requirements into data science solutions.
- Build and maintain data pipelines, analytical datasets, and workflows.
- Monitor, evaluate, and continuously improve model performance in production environments.
- Present actionable insights through dashboards, reports, and stakeholder discussions.
- Ensure data quality, reliability, and governance across manufacturing data sources.
- Drive the adoption of data-driven decision-making across plant operations.
Skills & Requirements
- 6+ years of experience in Data Science and Advanced Analytics.
- Hands-on experience in manufacturing, industrial, or plant environments.
- Strong working knowledge of Seeq (industrial analytics platform) for time-series analysis, including both Seeq Workbench and Seeq Data Lab (using the Seeq SPy library).
- Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
- Strong understanding of: Machine Learning (regression, anomaly detection, and predictive modeling); Statistical modeling and hypothesis-driven analysis; Time-series and sensor data analytics
- Experience building and deploying predictive models for: Predictive maintenance' Process optimization; Quality and yield improvement
- Ability to work with sensor data, process data, and operational datasets.
- Strong analytical thinking, troubleshooting, and root-cause analysis capabilities.
Good-to-Have Skills
- Experience in industries such as: Oil & Gas; Chemicals; Manufacturing
- Knowledge of MLOps, including model deployment, monitoring, and pipeline management.
- Exposure to optimization techniques for industrial processes.
- Exposure to cloud platforms such as Azure, AWS, or GCP.
- Familiarity with: Data visualization tools; Real-time and streaming data analytics; Data engineering concepts, including ETL, data pipelines, and data lakes